Real-time traffic information detection may provide the latest information on the traffic jam, traffic accident, estimated delay and alternative detour to the drivers, and assists the drivers to reset a new route and to estimate the arrival time through detected and estimated traffic information when a traffic jam occurs. Take a vehicle as an example. The traffic parameters, such as, (1) traffic flow density (congestion situation) and vehicle counts can be used to monitor the traffic condition on the section of a road or at intersection, (2) stalling time, queue length and average speed can be used to optimize the traffic light timing control, (3) single vehicle speed, lane change and safety distance can be used to warn rule-violating drivers, and (4) temporary parking event can be used to fast evacuate the jam caused by accidents.
In comparison with electromagnetic induction circles, radar speed detection guns and infra-red sensor, the photography-based camera detector has the advantages of obtaining a variety of information and detecting a plurality of lanes simultaneously. The vision-based traffic parameter detection techniques may be categorized as detection methods based on background subtraction and based on virtual wires. The background subtraction based detection method is shown as the exemplar in FIG. 1. Through image calibration technique, region of interest (ROI) calibration 120 is performed on inputted image frame 110, and background subtraction 130 is performed through background subtraction or frame difference method to detect the moving object. Then, the object tracking technique is used to perform object tracking 140, such as, tracking a vehicle. Finally, traffic parameter estimation 150 is performed, such as, vehicle count or speed estimation.
For the prior arts on background subtraction based detection methods, such as some methods to detect the edges of the captured digital image and learn the edges to capture the part of moving object for shadow removal and labeling connected elements, and then to perform region merge and vehicle tracking to obtain the traffic parameters. Some methods use background subtraction to generate the difference image representing moving object, divide the moving object into a plurality of regions, and analyze the validity and invalidity of the regions in order to eliminate the invalid regions and cluster valid regions for moving object tracking. Some methods use the difference between the current image and the background image to detect foreground object, use shadow removal and labeling connected elements to obtain a single vehicle object, and then use color information as object related rule for vehicle tracking. Some methods use two cameras to obtain the signal correlation and treat the displacement at the maximum correlation as the vehicle moving time.
FIG. 2 shows an exemplary schematic view of the virtual wire based detection method. Virtual wires 210, such as, detection window or detection line, are set on the image, and triggering conditions are set to determine whether vehicles have passed the virtual detection window, for example, detecting the vehicle entry event 220 and detecting the correlation 230 of entry and exit detection window, for the reference of estimating the traffic parameters, such as, vehicle count or traffic flow density. FIG. 3 shows an exemplary schematic view of setting virtual wires on an image 310, where two virtual wires, i.e., detection windows 332, 334, are set on vehicle lane 340. The temporal correlation between the entry and exit detection windows 332, 334 on the cross-sectional axis 350 of the image may be used to estimate the average speed on vehicle lane 340. In comparison with the background subtraction method, the detection methods based on virtual wires are able to obtain more stable vehicle detection. However, this type of detection methods do not track object, such as, single vehicle speed estimation, lane change and safety distance.
Among the conventional prior arts of virtual wire-based detection methods, some methods analyze the roads from a bird-eye's view map, register the front and rear features of the vehicle as the template, and use pattern-matching to track the vehicle when updating the template to improve the accuracy of traffic flow detection. Some methods set detection windows as the vehicle passing event detection and take the day/night situation into account, such as, edge features of the image is used for day and headlight detection is used for the night. Some methods compute the logarithmic gray-scale spectrum of the ROI of each lane in the captured image, and compute the difference with the reference logarithmic gray-scale spectrum at the high frequency to identify whether vehicles are present at the ROI on vehicle lane to compute the traffic flow.
The contemporary traffic parameter detection technologies usually suffer high cost of installation and maintenance, a large amount of computation, difficulty in vehicle detection caused by environmental light, shadow or unsteady camera, or difficulty in vehicle tracking due to the incapability to precisely determine a region of a single car. Therefore, the traffic parameter detection mechanism must be capable of tracking a single object, improve the precision of object counting and tracking, and improve the stability of traffic parameter estimation through object tracking so as to extract a variety of traffic parameters for the real-time application of traffic surveillance.